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752427134b0e91cf94a8188d21e3":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"cells":[{"cell_type":"markdown","source":["Copyright 2022 Google LLC. SPDX-License-Identifier: Apache-2.0\n","\n","Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n","\n","https://www.apache.org/licenses/LICENSE-2.0\n","\n","Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."],"metadata":{"id":"D_9iZ6bqIdJK"}},{"cell_type":"markdown","source":["# Experiment: Reactive Controllers on Toy Tasks\n","\n","This notebook is a part of the open-source code release associated with the paper:\n","\n","[Code as Policies: Language Model Programs for Embodied Control](https://code-as-policies.github.io/)\n","\n","This notebook gives the results corresponding to Seciont IV.D in the paper which generates reactive controllers for toy tasks.\n","\n","1) Please obtain an OpenAI API Key here:\n","https://openai.com/blog/openai-api/\n","\n","2) Gain Codex access by joining the waitlist here:\n","https://openai.com/blog/openai-codex/\n","\n","Once you have Codex access you can use `code-davinci-002` as the `model_name`. Using the GPT-3 model (`text-dainvci-002`) is also ok, but performance won't be as good (there will be more code logic errors).\n"],"metadata":{"id":"unchkbppIdG1"}},{"cell_type":"code","source":["openai_api_key = 'YOUR KEY HERE'\n","model_name = 'code-davinci-002' # 'text-davinci-002'"],"metadata":{"id":"qZilfRTTIdR_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Setup"],"metadata":{"id":"4xK4yAZ1c5gf"}},{"cell_type":"code","source":["! pip install numpy scipy shapely openai pygments > /dev/null 2>&1\n","\n","import openai\n","openai.api_key = openai_api_key"],"metadata":{"id":"ItA4urSZdaRn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from copy import copy\n","from time import sleep\n","from tqdm.auto import trange\n","\n","import ast\n","import astunparse\n","\n","from pygments import highlight\n","from pygments.lexers import PythonLexer\n","from pygments.formatters import TerminalFormatter\n","\n","def exec_safe(code_str, gvars, lvars):\n","  banned_phrases = ['import', '__']\n","  for phrase in banned_phrases:\n","    assert phrase not in code_str\n","  \n","  empty_fn = lambda *args, **kwargs: None\n","  custom_gvars = merge_dicts([\n","      gvars,\n","      {'exec': empty_fn, 'eval': empty_fn}\n","  ])\n","  exec(code_str, custom_gvars, lvars)\n","\n","default_query_kwargs = {\n","    'engine': model_name,\n","    'max_tokens': 512,\n","    'temperature': 0,\n","}\n","\n","def lmp(base_prompt, query, stop_tokens=None, log=True, return_response=False, query_kwargs=None):\n","    new_prompt = f'{base_prompt}\\n{query}'\n","    use_query_kwargs = copy(default_query_kwargs)\n","    if query_kwargs is not None:\n","      use_query_kwargs.update(query_kwargs)\n","    response = openai.Completion.create(\n","        prompt=new_prompt, stop=stop_tokens, **use_query_kwargs\n","    )['choices'][0]['text'].strip()\n","\n","    if log:\n","      print(query)\n","      print(response)\n","\n","    if return_response:\n","      return response\n","\n","def lmp_fgen(prompt, f_name, f_sig, stop_tokens=['# define function:', '# example:'], recurse=False, \n","             context_vars=None, bug_fix=False, log=True, return_src=False, query_kwargs=None, info=''):\n","    query = f'# define function: {f_sig}.'\n","    if info:\n","      query = f'{query}\\n# info: {info}.'\n","    f_src = lmp(prompt, query, stop_tokens=stop_tokens, log=False, return_response=True, query_kwargs=query_kwargs)\n","    if bug_fix:\n","        f_src = openai.Edit.create(\n","          model='code-davinci-edit-001',\n","          input='# ' + f_src,\n","          temperature=0,\n","          instruction=\"Fix the bug if there is one. Improve readability. Keep same inputs and outputs. Only small changes. No comments.\",\n","        )['choices'][0]['text'].strip()\n","\n","    if context_vars is None:\n","        context_vars = {}\n","    gvars = context_vars\n","    lvars = {}\n","\n","    f_success = True\n","    try:\n","      exec_safe(f_src, gvars, lvars)\n","      f = lvars[f_name]\n","    except Exception as e:\n","      print(e)\n","      f = lambda *args, **kargs: None   \n","      f_success = False \n","\n","    if recurse and f_success:\n","      # recursively define child_fs in the function body if needed\n","      f_def_body = astunparse.unparse(ast.parse(f_src).body[0].body)\n","      potential_child_fs, potential_child_f_sigs = {}, {}\n","      f_parser = FunctionParser(potential_child_fs, potential_child_f_sigs)\n","      f_parser.visit(ast.parse(f_def_body))\n","      for potential_child_f_name, potential_child_f_sig in potential_child_f_sigs.items():\n","        if potential_child_f_name in potential_child_fs:\n","          potential_child_fs[potential_child_f_name] = potential_child_f_sig\n","\n","      child_fs, child_f_srcs = {}, {}\n","      for child_f_name, child_f_sig in potential_child_fs.items():\n","        all_vars = merge_dicts([context_vars, child_fs])\n","        if not var_exists(child_f_name, all_vars):\n","          child_f, child_f_src = lmp_fgen(\n","              prompt, child_f_name, child_f_sig, \n","              stop_tokens=stop_tokens, \n","              context_vars=all_vars, \n","              bug_fix=bug_fix,\n","              log=False, \n","              recurse=True,\n","              return_src=True,\n","              query_kwargs=query_kwargs\n","            )\n","\n","          child_fs[child_f_name] = child_f\n","          child_f_srcs[child_f_name] = child_f_src\n","\n","      if len(child_fs) > 0:\n","        # redefine parent f so newly created child_fs are in scope\n","        gvars = merge_dicts([context_vars, child_fs])\n","        lvars = {}\n","      \n","        exec_safe(f_src, gvars, lvars)\n","        \n","        f = lvars[f_name]\n","\n","    if log:\n","        to_print = highlight(f'{query}\\n{f_src}', PythonLexer(), TerminalFormatter())\n","        print(f'LMP FGEN created:\\n\\n{to_print}\\n')\n","\n","    if return_src:\n","        return f, f_src\n","    return f\n","\n","class FunctionParser(ast.NodeTransformer):\n","\n","    def __init__(self, fs, f_assigns):\n","      super().__init__()\n","      self._fs = fs\n","      self._f_assigns = f_assigns\n","\n","    def visit_Call(self, node):\n","        self.generic_visit(node)\n","        if isinstance(node.func, ast.Name):\n","            f_sig = astunparse.unparse(node).strip()\n","            f_name = astunparse.unparse(node.func).strip()\n","            self._fs[f_name] = f_sig\n","        return node\n","\n","    def visit_Assign(self, node):\n","        self.generic_visit(node)\n","        if isinstance(node.value, ast.Call):\n","            assign_str = astunparse.unparse(node).strip()\n","            f_name = astunparse.unparse(node.value.func).strip()\n","            self._f_assigns[f_name] = assign_str\n","        return node\n","\n","def var_exists(name, all_vars):\n","    try:\n","        eval(name, all_vars)\n","    except:\n","        exists = False\n","    else:\n","        exists = True\n","    return exists\n","\n","def merge_dicts(dicts):\n","    return {\n","        k : v \n","        for d in dicts\n","        for k, v in d.items()\n","    }"],"metadata":{"id":"flDzBTmadcPA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["prompt_f_gen = '''\n","import numpy as np\n","from shapely.geometry import *\n","from shapely.affinity import *\n","from utils import get_obj_outer_pts_np\n","\n","# define function: total = get_total(xs=numbers).\n","def get_total(xs):\n","    return np.sum(xs)\n","\n","# define function: y = eval_line(x, slope, y_intercept=0).\n","def eval_line(x, slope, y_intercept):\n","    return x * slope + y_intercept\n","\n","# define function: pt_np = get_pt_to_the_left(pt_np, dist).\n","def get_pt_to_the_left(pt_np, dist):\n","    delta = np.array([-dist, 0])\n","    return translate_pt_np(pt_np, delta=delta)\n","\n","# define function: pt_np = get_pt_to_the_top(pt_np, dist).\n","def get_pt_to_the_top(pt_np, dist):\n","    delta = np.array([0, dist])\n","    return translate_pt_np(pt_np, delta=delta)\n","\n","# define function line = make_line_by_length(length=x).\n","def make_line_by_length(length):\n","  start = np.array([0, 0])\n","  end = np.array([length, 0])\n","  line = make_line(start=start, end=end)\n","  return line\n","\n","# define function: line = make_vertical_line_by_length(length=x).\n","def make_vertical_line_by_length(length):\n","  line = make_line_by_length(length)\n","  vertical_line = rotate(line, 90)\n","  return vertical_line\n","\n","# define function: pt = interpolate_line(line, t=0.5).\n","def interpolate_line(line, t):\n","  pt = line.interpolate(t, normalized=True)\n","  return np.array(pt.coords[0])\n","\n","# example: scale a line by 2 around the centroid.\n","line = make_line_by_length(1)\n","new_shape = scale(line, xfact=2, yfact=2, origin='centroid')\n","\n","# example: rotate a point around origin by 45 degrees.\n","pt = Point([1,1])\n","new_pt = rotate(pt, 45, origin=[0, 0])\n","\n","# example: getting object points of object0.\n","pts_np = get_obj_outer_pts_np('object0')\n","'''.strip()\n","\n","prompt_f_gen_exec = '''\n","import numpy as np\n","from shapely.geometry import *\n","from shapely.affinity import *\n","\n","# define function: total = get_total(xs=numbers).\n","def get_total(xs):\n","    return np.sum(xs)\n","\n","# define function: y = eval_line(x, slope, y_intercept=0).\n","def eval_line(x, slope, y_intercept):\n","    return x * slope + y_intercept\n","\n","# define function: pt = get_pt_to_the_left(pt, dist).\n","def get_pt_to_the_left(pt, dist):\n","    return pt + [-dist, 0]\n","\n","# define function: pt = get_pt_to_the_top(pt, dist).\n","def get_pt_to_the_top(pt, dist):\n","    return pt + [0, dist]\n","\n","# define function line = make_line_by_length(length=x).\n","def make_line_by_length(length):\n","  line = LineString([[0, 0], [length, 0]])\n","  return line\n","\n","# define function: line = make_vertical_line_by_length(length=x).\n","def make_vertical_line_by_length(length):\n","  line = make_line_by_length(length)\n","  vertical_line = rotate(line, 90)\n","  return vertical_line\n","\n","# define function: pt = interpolate_line(line, t=0.5).\n","def interpolate_line(line, t):\n","  pt = line.interpolate(t, normalized=True)\n","  return np.array(pt.coords[0])\n","'''.strip()\n","\n","context_vars = {}\n","exec(prompt_f_gen_exec, context_vars)"],"metadata":{"id":"b0-Lf7uudmUs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# CartPole\n","\n","Modified from https://colab.research.google.com/drive/1rmjdGfWsC1w-fOkICvknaGSsOUQ8Gp49"],"metadata":{"id":"NB-K_dfGc78F"}},{"cell_type":"markdown","metadata":{"id":"odNaDE1zyrL2"},"source":["## Setup"]},{"cell_type":"code","metadata":{"id":"8-AxnvAVyzQQ"},"source":["# Rendering Dependencies\n","!pip install gym[all]==0.21 pyvirtualdisplay > /dev/null 2>&1\n","!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n","# Gym Dependencies\n","# !apt-get update > /dev/null 2>&1\n","# !apt-get install cmake > /dev/null 2>&1\n","# !pip install --upgrade setuptools 2>&1\n","# !pip install ez_setup > /dev/null 2>&1"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"pdb2JwZy4jGj"},"source":["import gym\n","from gym import logger as gymlogger\n","from gym.wrappers import Monitor\n","gymlogger.set_level(40) #error only\n","import tensorflow as tf\n","import numpy as np\n","import random\n","import matplotlib\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","import math\n","import glob\n","import io\n","import base64\n","from IPython.display import HTML\n","\n","from IPython import display as ipythondisplay\n","\n","# Google Colab needs to render the environment to a virtual display\n","# we will record this as a video and play it after the training has finished\n","from pyvirtualdisplay import Display\n","display = Display(visible=0, size=(1400, 900))\n","display.start()\n","\n","\"\"\"\n","Utility functions to enable video recording of gym environment and displaying it\n","To enable video, just do \"env = wrap_env(env)\"\"\n","\"\"\"\n","\n","def show_video():\n","  mp4list = glob.glob('video/*.mp4')\n","  if len(mp4list) > 0:\n","    mp4 = mp4list[-1]\n","    video = io.open(mp4, 'r+b').read()\n","    encoded = base64.b64encode(video)\n","    ipythondisplay.display(HTML(data='''<video alt=\"test\" autoplay \n","                controls style=\"height: 400px;\">\n","                <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" />\n","             </video>'''.format(encoded.decode('ascii'))))\n","  else: \n","    print(\"Could not find video\")\n","    \n","def wrap_env(env):\n","  env = Monitor(env, './video', force=True)\n","  return env"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"W3BGbWOu179M"},"source":["## Generate Policy\n"]},{"cell_type":"code","source":["f_name = 'keep_pole_upright_with_pd_control'\n","f_sig = 'direction = keep_pole_upright_with_pd_control(x, x_dot, theta, theta_dot)'\n","info = 'direction is 1 if going right, 0 if going left'\n","\n","policy = lmp_fgen(prompt_f_gen, f_name, f_sig, recurse=True, context_vars=context_vars, info=info)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNtNVaMCdCrE","executionInfo":{"status":"ok","timestamp":1660332463619,"user_tz":240,"elapsed":10788,"user":{"displayName":"Jacky Liang","userId":"05524594537381871813"}},"outputId":"3a91df1d-89b5-41f2-8dba-d6c6c0fac2ad"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["LMP FGEN created:\n","\n","\u001b[37m# define function: direction = keep_pole_upright_with_pd_control(x, x_dot, theta, theta_dot).\u001b[39;49;00m\n","\u001b[37m# info: info: direction is 1 if going right, 0 if going left.\u001b[39;49;00m\n","\u001b[34mdef\u001b[39;49;00m \u001b[32mkeep_pole_upright_with_pd_control\u001b[39;49;00m(x, x_dot, theta, theta_dot):\n","    \u001b[37m# define constants.\u001b[39;49;00m\n","    kp = \u001b[34m1\u001b[39;49;00m\n","    kd = \u001b[34m1\u001b[39;49;00m\n","    \u001b[37m# define direction.\u001b[39;49;00m\n","    direction = \u001b[34m1\u001b[39;49;00m\n","    \u001b[37m# define error.\u001b[39;49;00m\n","    error = theta\n","    \u001b[37m# define error_dot.\u001b[39;49;00m\n","    error_dot = theta_dot\n","    \u001b[37m# define control.\u001b[39;49;00m\n","    control = kp * error + kd * error_dot\n","    \u001b[37m# define direction.\u001b[39;49;00m\n","    \u001b[34mif\u001b[39;49;00m control < \u001b[34m0\u001b[39;49;00m:\n","        direction = \u001b[34m0\u001b[39;49;00m\n","    \u001b[34mreturn\u001b[39;49;00m direction\n","\n","\n"]}]},{"cell_type":"markdown","source":["## Run Policy"],"metadata":{"id":"uuB4UW_hdwah"}},{"cell_type":"code","metadata":{"id":"8nj5sjsk15IT","colab":{"base_uri":"https://localhost:8080/","height":439},"executionInfo":{"status":"ok","timestamp":1660332541608,"user_tz":240,"elapsed":7408,"user":{"displayName":"Jacky Liang","userId":"05524594537381871813"}},"outputId":"9dbaf70d-379f-4eef-b7a8-31b64ec3bcf4"},"source":["env = wrap_env(gym.make('CartPole-v1'))\n","observation = env.reset()\n","timestep = 0\n","\n","while True:\n","  env.render()\n","  \n","  action = policy(*observation)\n","  \n","  observation, reward, done, info = env.step(action) \n","  timestep += 1\n","  \n","  if done:\n","    break\n","\n","print(\"run took \" + str(timestep) + \" timesteps\")\n","env.close()\n","show_video()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["run took 500 timesteps\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<video alt=\"test\" autoplay \n","                controls style=\"height: 400px;\">\n","                <source 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type=\"video/mp4\" />\n","             </video>"]},"metadata":{}}]},{"cell_type":"markdown","source":["# End-Effector Impedance Control"],"metadata":{"id":"QP9d2jahfYYC"}},{"cell_type":"markdown","source":["## Setup"],"metadata":{"id":"uwDRnyK9g1Ah"}},{"cell_type":"code","source":["! pip install numpy scipy shapely astunparse pygments openai numpy-quaternion > /dev/null 2>&1\n","! pip install imageio==2.4.1 imageio-ffmpeg pybullet moviepy > /dev/null 2>&1\n","\n","import pybullet\n","import pybullet_data\n","import cv2\n","from google.colab.patches import cv2_imshow\n","from moviepy.editor import ImageSequenceClip\n","import os\n","import itertools\n","\n","# Download PyBullet assets.\n","if not os.path.exists('ur5e/ur5e.urdf'):\n","  !gdown --id 1Cc_fDSBL6QiDvNT4dpfAEbhbALSVoWcc\n","  !gdown --id 1yOMEm-Zp_DL3nItG9RozPeJAmeOldekX\n","  !gdown --id 1GsqNLhEl9dd4Mc3BM0dX3MibOI1FVWNM\n","  !unzip ur5e.zip\n","  !unzip robotiq_2f_85.zip\n","  !unzip bowl.zip"],"metadata":{"id":"pTRFEdpDg2E4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","import threading\n","import time\n","import pybullet as p\n","import quaternion\n","from tqdm.auto import tqdm\n","\n","class RobotEnv():\n","\n","  def __init__(self):\n","    self.dt = 1/480\n","    self.sim_step = 0\n","\n","    # Configure and start PyBullet.\n","    # python3 -m pybullet_utils.runServer\n","    # p.connect(p.SHARED_MEMORY)  # p.GUI for local GUI.\n","    p.connect(p.DIRECT)  # p.GUI for local GUI.\n","    p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0)\n","    p.setPhysicsEngineParameter(enableFileCaching=0)\n","    assets_path = os.path.dirname(os.path.abspath(\"\"))\n","    p.setAdditionalSearchPath(assets_path)\n","    p.setAdditionalSearchPath(pybullet_data.getDataPath())\n","    p.setTimeStep(self.dt)\n","\n","    self.home_joints = (np.pi / 2, -np.pi / 2, np.pi / 2, -np.pi / 2, 3 * np.pi / 2, 0)  # Joint angles: (J0, J1, J2, J3, J4, J5).\n","    self.home_ee_euler = (np.pi, 0, np.pi)  # (RX, RY, RZ) rotation in Euler angles.\n","    self.ee_link_id = 9  # Link ID of UR5 end effector.\n","\n","    p.resetSimulation(p.RESET_USE_DEFORMABLE_WORLD)\n","    p.setGravity(0, 0, -9.8)\n","    self.cache_video = []\n","\n","    # Temporarily disable rendering to load URDFs faster.\n","    p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0)\n","\n","    # Add robot.\n","    p.loadURDF(\"plane.urdf\", [0, 0, -0.001])\n","    self.robot_id = p.loadURDF(\"ur5e/ur5e.urdf\", [0, 0, 0], flags=p.URDF_USE_MATERIAL_COLORS_FROM_MTL)\n","    self.ghost_id = p.loadURDF(\"ur5e/ur5e.urdf\", [0, 0, -10])  # For forward kinematics.\n","    self.joint_ids = [p.getJointInfo(self.robot_id, i) for i in range(p.getNumJoints(self.robot_id))]\n","    self.joint_ids = [j[0] for j in self.joint_ids if j[2] == p.JOINT_REVOLUTE]\n","\n","    self.reset()\n","\n","  def reset(self):\n","    # Move robot to home configuration.\n","    for i in range(len(self.joint_ids)):\n","      p.resetJointState(self.robot_id, self.joint_ids[i], self.home_joints[i])\n","\n","    # Re-enable rendering.\n","    p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 1)\n","\n","    for _ in range(200):\n","      p.stepSimulation()\n","\n","  def get_ee_pos(self):\n","    ee_xyz = np.float32(p.getLinkState(self.robot_id, self.ee_link_id)[0])\n","    return ee_xyz\n","\n","  def get_ee_lin_vel(self):\n","    return np.float32(p.getLinkState(self.robot_id, self.ee_link_id, computeLinkVelocity=1)[6])\n","\n","  def get_ee_ang_vel(self):\n","      return np.float32(p.getLinkState(self.robot_id, self.ee_link_id, computeLinkVelocity=1)[7])\n","\n","  def get_motor_joint_states(self):\n","    joint_states = p.getJointStates(self.robot_id, range(p.getNumJoints(self.robot_id)))\n","    joint_infos = [p.getJointInfo(self.robot_id, i) for i in range(p.getNumJoints(self.robot_id))]\n","    joint_states = [j for j, i in zip(joint_states, joint_infos) if i[3] > -1]\n","    joint_positions = [state[0] for state in joint_states]\n","    joint_velocities = [state[1] for state in joint_states]\n","    joint_torques = [state[3] for state in joint_states]\n","    return joint_positions, joint_velocities, joint_torques\n","\n","  def get_motor_joint_idxs(self):\n","    joint_infos = [p.getJointInfo(self.robot_id, i) for i in range(p.getNumJoints(self.robot_id))]\n","    motor_joint_idxs = [idx for idx, joint_info in enumerate(joint_infos) if joint_info[3] > -1]\n","    return motor_joint_idxs    \n","\n","  def get_jacobian(self):\n","    mpos, mvel, mtorq = self.get_motor_joint_states()\n","    result = p.getLinkState(env.robot_id,\n","                            env.ee_link_id,\n","                            computeLinkVelocity=1,\n","                            computeForwardKinematics=1)\n","    link_trn, link_rot, com_trn, com_rot, frame_pos, frame_rot, link_vt, link_vr = result\n","\n","    zero_vec = [0.0] * len(mpos)\n","    jac_t, _ = p.calculateJacobian(env.robot_id, env.ee_link_id, com_trn, mpos, mvel, zero_vec)\n","    return np.array(jac_t)\n","\n","  def follow_wps(self, wps, controller, K_x_mat, D_x_mat, render=False, high_res=False):\n","    if render:\n","      self.cache_video = []\n","\n","    motor_joint_idxs = self.get_motor_joint_idxs()\n","\n","    for x_goal in tqdm(wps):\n","      x_curr = self.get_ee_pos()\n","      x_dot = self.get_ee_lin_vel()\n","      J = self.get_jacobian()\n","\n","      tau = controller(x_curr, x_goal, x_dot, K_x_mat, D_x_mat, J)\n","\n","      p.setJointMotorControlArray(self.robot_id, motor_joint_idxs, p.TORQUE_CONTROL, forces=tau)\n","      self.step_sim_and_render(render=render, high_res=high_res)\n","\n","  def step_sim_and_render(self, render=False, high_res=False):\n","    p.stepSimulation()\n","    self.sim_step += 1\n","\n","    # Render current image at 24 FPS.\n","    # if self.sim_step % 60 == 0 and render:\n","    if self.sim_step % 20 == 0 and render:\n","      self.cache_video.append(self.get_camera_image(high_res=high_res))\n","\n","  def get_camera_image(self, high_res):\n","    if not high_res:\n","      image_size = (240, 240)\n","      intrinsics = (120., 0, 120., 0, 120., 120., 0, 0, 1)\n","    else:\n","      image_size=(720, 720)\n","      intrinsics=(360., 0, 360., 0, 360., 360., 0, 0, 1)\n","    color, _, _, _, _ = self.render_image(image_size, intrinsics)\n","    return color\n","\n","  def render_image(self, image_size=(720, 720), intrinsics=(360., 0, 360., 0, 360., 360., 0, 0, 1)):\n","    # Camera parameters.\n","    position = (0, -0.85, 0.8)\n","    orientation = (np.pi / 4 + np.pi / 48, np.pi, np.pi)\n","    orientation = p.getQuaternionFromEuler(orientation)\n","    zrange = (0.01, 10.)\n","    noise=True\n","\n","    # OpenGL camera settings.\n","    lookdir = np.float32([0, 0, 1]).reshape(3, 1)\n","    updir = np.float32([0, -1, 0]).reshape(3, 1)\n","    rotation = p.getMatrixFromQuaternion(orientation)\n","    rotm = np.float32(rotation).reshape(3, 3)\n","    lookdir = (rotm @ lookdir).reshape(-1)\n","    updir = (rotm @ updir).reshape(-1)\n","    lookat = position + lookdir\n","    focal_len = intrinsics[0]\n","    znear, zfar = (0.01, 10.)\n","    viewm = p.computeViewMatrix(position, lookat, updir)\n","    fovh = (image_size[0] / 2) / focal_len\n","    fovh = 180 * np.arctan(fovh) * 2 / np.pi\n","\n","    # Notes: 1) FOV is vertical FOV 2) aspect must be float\n","    aspect_ratio = image_size[1] / image_size[0]\n","    projm = p.computeProjectionMatrixFOV(fovh, aspect_ratio, znear, zfar)\n","\n","    # Render with OpenGL camera settings.\n","    _, _, color, depth, segm = p.getCameraImage(\n","        width=image_size[1],\n","        height=image_size[0],\n","        viewMatrix=viewm,\n","        projectionMatrix=projm,\n","        shadow=1,\n","        flags=p.ER_SEGMENTATION_MASK_OBJECT_AND_LINKINDEX,\n","        renderer=p.ER_BULLET_HARDWARE_OPENGL)\n","\n","    # Get color image.\n","    color_image_size = (image_size[0], image_size[1], 4)\n","    color = np.array(color, dtype=np.uint8).reshape(color_image_size)\n","    color = color[:, :, :3]  # remove alpha channel\n","    if noise:\n","      color = np.int32(color)\n","      color += np.int32(np.random.normal(0, 3, color.shape))\n","      color = np.uint8(np.clip(color, 0, 255))\n","\n","    # Get depth image.\n","    depth_image_size = (image_size[0], image_size[1])\n","    zbuffer = np.float32(depth).reshape(depth_image_size)\n","    depth = (zfar + znear - (2 * zbuffer - 1) * (zfar - znear))\n","    depth = (2 * znear * zfar) / depth\n","    if noise:\n","      depth += np.random.normal(0, 0.003, depth.shape)\n","\n","    intrinsics = np.float32(intrinsics).reshape(3, 3)\n","    return color, depth, position, orientation, intrinsics\n","\n","  def show_video(self, loop=0):\n","    debug_clip = ImageSequenceClip(env.cache_video, fps=24)\n","    display(debug_clip.ipython_display(autoplay=1, loop=loop))"],"metadata":{"id":"PV5rfIgwfdU7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Generate Controller"],"metadata":{"id":"Z5aXY6mgffDH"}},{"cell_type":"code","source":["f_name = 'ee_impedance_control'\n","f_sig = 'tau = ee_impedance_control(x_curr, x_goal, x_dot, K_x_mat, D_x_mat, J)'\n","controller = lmp_fgen(prompt_f_gen, f_name, f_sig, recurse=True, context_vars=context_vars)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-x-Q70mY-o0m","executionInfo":{"status":"ok","timestamp":1660843407359,"user_tz":240,"elapsed":6020,"user":{"displayName":"Jacky Liang","userId":"05524594537381871813"}},"outputId":"9545d959-c318-4d22-ef43-8e8afca83df3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["LMP FGEN created:\n","\n","\u001b[37m# define function: tau = ee_impedance_control(x_curr, x_goal, x_dot, K_x_mat, D_x_mat, J).\u001b[39;49;00m\n","\u001b[34mdef\u001b[39;49;00m \u001b[32mee_impedance_control\u001b[39;49;00m(x_curr, x_goal, x_dot, K_x_mat, D_x_mat, J):\n","    x_err = x_goal - x_curr\n","    x_dot_err = -x_dot\n","    tau = np.matmul(J.T, np.matmul(K_x_mat, x_err) + np.matmul(D_x_mat, x_dot_err))\n","    \u001b[34mreturn\u001b[39;49;00m tau\n","\n","\n"]}]},{"cell_type":"markdown","source":["## Run Controller"],"metadata":{"id":"bzlNHtSxWjmG"}},{"cell_type":"code","source":["env = RobotEnv()"],"metadata":{"id":"_6p5lSoYGzl4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["env.reset()\n","init_pos = env.get_ee_pos()\n","\n","K_x_mat = np.diag([5e4] * 3)\n","D_x_mat = np.diag([1e-3] * 3)\n","goal_horizon = 200\n","\n","wp_goals = np.array([\n","    [-0.1, -0.1, 0],\n","    [0.2, 0, 0.1],\n","    [-0.1, 0.1, -0.1]\n","])\n","wps = [init_pos]\n","for wp_goal_pos in wp_goals:\n","  prev_pos = wps[-1]\n","  wps.extend(np.linspace(prev_pos, prev_pos + wp_goal_pos, goal_horizon).tolist())\n","\n","env.follow_wps(wps, controller, K_x_mat, D_x_mat, render=True, high_res=True)\n","env.show_video()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":791,"referenced_widgets":["81c8deabedd54413bbe02cb525d6acc0","5ae182fc294f44b1b69e4a5af5dd939a","c6d9268b03ee469eb06cab68caec117e","2a9614c8dd5f49ae99e002deaa8180f4","6701a8b858aa4a6fa37383b1cc90ce9e","58eb46702c4047f69b3d657306d1d114","3f5b1a0286714615a6cdd2b1aa87bf7f","671becff8023467b955eaef05467a94f","5e9951df31ac4a1d8fcdcd1db65af708","a9cb4d94f05e4565ae2d2e13e722a9b7","dfe4752427134b0e91cf94a8188d21e3"]},"id":"rlpMejCtIDlG","executionInfo":{"status":"ok","timestamp":1660330851581,"user_tz":240,"elapsed":20888,"user":{"displayName":"Jacky Liang","userId":"05524594537381871813"}},"outputId":"f620ae9b-5a40-4564-d3be-d22e914d0996"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/601 [00:00<?, 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